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Need advice for training a YOLOv5-obb model #12959
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@yasmine-lkl hello! 🚀 Great to hear about your project and your proactive steps with YOLOv5-obb for drone-captured image detection. Training models on small objects is a known challenge, but your approach is commendable. For small datasets like yours (150-200 images), ensuring high-quality annotations is key. The more precise your annotations, especially with oriented bounding boxes (OBB), the better your results will likely be. However, the size of your dataset is indeed small, which can impact the model's effectiveness. To enhance your model's performance, consider:
Remember, the journey to an optimal model often involves a lot of trial and testing. Keep experimenting with different configurations, and don't hesitate to revisit your dataset for possible improvements. Best of luck with your project! If you have more questions or need further assistance, feel free to ask. 🌟 |
@glenn-jocher Thank you so much for taking the time to respond to my message and for your invaluable advice! 🙏 I'm thrilled to hear your suggestions regarding data augmentation. Is it possible to perform data augmentation directly on Roboflow? Additionally, I'm considering using tiling to zoom in on my photos, but I'm concerned about how this might affect the annotations. Do you have any solutions for this issue? I'm also curious about integrating other methods such as self-supervised learning or cross-validation to optimize my model. Would these approaches be beneficial in my case? Adding pre-trained data isn't feasible since the objects I aim to detect, such as Velux windows, chimneys, and ventilation systems, aren't found in pre-trained datasets. Ultimately, my goal is to test the tool on orthophotos and integrate it into AutoCAD software for automatic detection of objects in orthophotos. If you have any further advice or suggestions, I would be incredibly grateful! Thank you once again for your support and guidance. 🚀 |
Hello again, I am reaching out for your guidance regarding an issue I am encountering with my YOLOv8-obb model. Here is a summary of my training process: I manually annotated 200 images. model = YOLO('/content/runs/obb/train/weights/best.pt') images_directory = "/content/datasets/roboflow/test/images" if os.path.exists(images_directory):
else: Despite the seemingly successful training and validation, the above script yields no detections for my test images. Is this behavior normal? Could you provide any recommendations on what I might modify to achieve better results? Additionally, I am considering integrating methods like cross-validation, self-supervised learning, or SAHI. Are these approaches supported by YOLOv8-obb, and would you recommend their usage in this context? Thank you in advance for your assistance. I look forward to your insights. Best regards |
@yasmine-lkl hello, It sounds like you're making great progress with your YOLOv8-obb model, but encountering a hiccup during testing is indeed frustrating. Given that your training and validation phases showed promising results but no detections are found during testing, here are a few things to consider:
Regarding integrating methods like cross-validation, self-supervised learning, or SAHI, these are advanced techniques that can potentially improve model robustness:
Experimenting with these methods depends on your specific needs and the complexity you're ready to manage. Keep iterating and fine-tuning based on the results you observe. Best of luck, and keep pushing forward! 🚀 |
Hello again :) I wanted to share with you the results I've obtained while testing yolov8x-obb. Enclosed is a screenshot illustrating the outcomes, showing a somewhat satisfactory detection of objects. Notably, I haven't implemented any of the methods I proposed in my previous comment. Consequently, I'm uncertain whether to assert that my model effectively detects objects and proceed to deployment or if further optimization is necessary. My primary query revolves around determining whether my model is adequately optimized and how I can manipulate hyperparameters to achieve optimization. While I understand the concept of commencing with a relatively high learning rate and subsequently decreasing it after a certain number of epochs, I'm unsure about the specifics. Should I provide a precise initial learning rate (lr0), and will the model automatically adjust it after each epoch? Additionally, I seek guidance on selecting the appropriate batch size and other hyperparameters, as well as interpreting the resultant curves. I genuinely appreciate your assistance and guidance in aiding community to progress in this field. Your insights have been invaluable, and I am sincerely grateful for your ongoing support. |
Hello @yasmine-lkl, Great to see your progress with the YOLOv8x-obb model! From your results, it looks like you're on the right track. Regarding optimization and deployment:
Keep testing with varied data to ensure robustness before full deployment. Your diligence in refining the model will pay off! Best of luck, and keep up the great work! 🚀 |
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Question
Hello,
I'm currently working on my internship project focusing on object detection in images captured by drones. I'm facing some challenges and would like to seek your advice and suggestions.
Firstly, the objects I'm aiming to detect are quite small, and I haven't found a pre-trained dataset that matches my requirements online. To overcome this challenge, I've decided to use YOLOv5-obb and have started annotating my images on Roboflow. I'd like to emphasize that I'm using instance segmentation instead of traditional object detection on Roboflow. This decision allows me to leverage the "polygon tool" for accurate object orientation. When I export my annotations, they will be in the "oriented bounding boxes YOLOv5" format.
However, the annotation process is taking a significant amount of time, and I'm wondering if annotating 150 to 200 images would be sufficient to train my model and achieve effective results. Additionally, I'm a novice in using YOLO, and I'm seeking advice on how to optimize my model.
If you have any recommendations, tips, or methods to improve the efficiency of my model, I would be extremely grateful to hear them.
Thank you in advance for your valuable assistance.
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